The total capital expenditures required to keep pace with consumer demands being placed on wireless communication networks is increasing significantly. As wireless networks become more complex and more ubiquitous, the costs associated with establishing, provisioning, and maintaining these networks continues to rise. One of the reasons these costs continue to rise is the high level of skill required to establish, manage, and maintain these networks. From the nascent stages of network design through the more mundane tasks of ensuring operability, highly skilled individuals play significant roles in the continued functioning of our wireless networks.
Recently, wireless communication providers have begun to automate some of the tasks associated with network provisioning and maintenance using SON techniques that self-configure, self-optimize, and self-heal. SON solutions are primarily being implemented in either the LTE access portion of eNodeBs, or within a central management node, but not on the backhaul portion of the eNodeBs. An eNodeB is the combined radio interface and radio network controller in an LTE station.
These prior implementations have drawbacks because, for example, when SON techniques occur only at the eNodeB level, each eNodeB has a limited view of the entire network. In most networks, an eNodeB does not see network operational parameters and environmental conditions external to itself. As a result, eNodeBs are typically limited in their ability to make dynamic operational changes. Similarly, when a central management node implements SON techniques, the typical flow involves a network management node asking an operations management node if any of the eNodeBs under its operational control are experiencing interference. In order to answer that question, the operational management node queries the eNodeBs under its control. Once it has received all of the responses from each of the eNodeBs under its management, it compiles these data and makes a determination about whether it should instruct any of the eNodeBs to change operational parameters.
The types of SON implementations currently being employed are primarily focused on auto-provisioning and interference mitigation. In current deployments, network resources such as Wi-Fi and 3G/4G antennas and Ethernet links are configured as either access or backhaul. Generally, that role persists for the life of the product. If a SON module could be used to make these roles more dynamic, networks could be more flexible and could provision themselves on-the-fly so as to overcome network isolation and maximize network efficiency.
In another example, if a certain portion of a frequency band has a transmit power restriction placed upon it, network operators typically do not use that portion of the band because it is challenging to create different power profiles for certain sub-channels or sub-carrier frequencies. Restrictions are typically placed on a portion of a frequency band to avoid interference. These restrictions can be e.g., adding a buffer zone, creating a guard band, and the like. The frequency-band nature of electronic components makes it difficult to control the power of certain sub-carrier frequencies, or sub-channels, within the band using static electronic components. If, however, SON techniques could be applied to this scenario, it would be possible to obtain more capacity out of frequency bands having restrictions.
While the concept of SON is clearly an attractive one given its ability to reduce costs and resolution time of issues when they arise in the network, there is a need to apply the principles of SON to a wider array of wireless communication protocols, frequency ranges, access and backhaul, and the like. Similarly, it is desirable to design SON modules that can integrate these disparate technologies, protocols, network pathways and the like by leveraging the strengths of each within a particular network in a hybridized fashion, and on a real-time basis. In addition, in a mesh network, where nodes are more autonomous than in traditional networks, adding SON capabilities to the mesh topology would enhance the performance of these networks as well.